Kalman filter based production control of a failure-prone single-machine single-product manufacturing system with imprecise demand and inventory information

An adaptive production control structure for failure-prone manufacturing systems under inventory and demand uncertainty is proposed. It contains estimation and forecasting modules incorporated into a control loop. The customer demand is unknown and its rate is composed of ramp-type, seasonal and random components. Information available to decision maker consists of imprecise inventory records, and the Kalman filter technique is used for estimating the inventory level and demand rate online from noisy inventory measurements. Estimates obtained are shown to converge to the actual values in stochastic sense. They are subsequently used for demand component forecasting, once the estimation errors become sufficiently small. A forecasting algorithm allows estimating ramp-type and seasonal demand components, together with their potential errors. Obtained estimates are incorporated into production control procedures, recently developed for manufacturing systems under variable and uncertain demand. Optimality conditions in the form of Hamilton-Jacobi-Bellman equations are obtained. A constructive numerical method for computing sub-optimal production policies is proposed and validated through numerical simulations.     

Predictive economic efficiency of combining nuclear power plants with autonomous hydrogen power complex

Currently, the United Energy System (UEC) of Russia is trending in the deficit of peak and half-peak capacity with a simultaneous increase in the number of nuclear power plants (NPPs), which will require the participation of the NPPs in the variable part of the schedule of electrical loads.In addition to the economic need to maintain the high-level utilization rate, there are technological limitations of maneuverability for NPPs.The authors developed an approach to solving this problem by combining with an environmentally friendly energy source – an autonomous hydrogen power complex, which includes thermal batteries and an additional multifunctional low-power steam turbine installation.The developed energy complex can also provide reliable reservation of electricity supply to consumers of their own needs of the nuclear power plant in case of complete blackout of the plant.The feasibility study of the main equipment of the autonomous hydrogen power complex, which is necessary for combining with a two-unit nuclear power plant with WWER-1000, has been evaluated.On the basis of the assessment of the inflation indicators of the Russian economy over the past 11 years, three variants of fuel cost dynamics and tariff rates for electricity (capacity) as well as the size of operating costs, including depreciation deductions to the main equipment, are defined, taking into account the current principles of price formation.The result is a value for accumulated net present value, depending on the ratio of the cost of the half-peak and off-peak electricity at different inflation rates.The positive economic effect of reducing the risk of the core damage accident, replacing the construction of the gas turbine unit as a maneuverable source of electricity in the power grid and increasing the income of the Russian federal budget from the savings of natural gas has been taken into account.The greatest economic efficiency is achieved with maximum projected inflation, which is associated with the maximum rate of discounting and the high rate of growth of electricity tariffs.Reducing the risk of the core damage accident ensures that the proposed approach is competitive in all the inflation options under consideration and the ratio of electricity tariffs.     

基于ARMA模型的双路径差异性分析

拟通过单向时延的历史记录对其未来趋势进行预测。首先,通过粗粒度和细粒度两种方式采集世界各地若干目标节点的单向时延作为原始数据;然后,结合多种预测模型的特点和原始数据的内在属性,提出一种基于ARMA模型的双路径差异性分析方法;最后选取合理的预测窗口对方法的准确性进行验证。结果表明,该预测方法是正确、合理的,与灰色预测模型相比能更有效地预测单项时延差序列。     

A non-parametric approach to demand forecasting in revenue management

In revenue management, the profitability of the inventory and pricing decisions rests on the accuracy of demand forecasts. However, whenever a product is no longer available, true demand may differ from registered bookings, thus inducing a negative bias in the estimation figures, as well as an artificial increase in demand for substitute products. In order to address these issues, we propose an original Mixed Integer Nonlinear Program (MINLP) to estimate product utilities as well as capturing seasonal effects. This behavioral model solely rests on daily registered bookings and product availabilities. Its outputs are the product utilities and daily potential demands, together with the expected demand of each product within any given time interval. Those are obtained via a tailored algorithm that outperforms two well-known generic software for global optimization.     

A novel intelligent approach for yaw position forecasting in wind energy systems

Yaw control systems orientate the rotor of a wind turbine into the wind direction, optimize the wind power generated by wind turbines and alleviate the mechanical stresses on a wind turbine. Regarding the advantages of yaw control systems, a k-nearest neighbor classifier (k-NN) has been developed in order to forecast the yaw position parameter at 10-min intervals in this study. Air temperature, atmosphere pressure, wind direction, wind speed, rotor speed and wind power parameters are used in 2, 3, 4, 5 and 6-dimensional input spaces. The forecasting model using Manhattan distance metric for k = 3 uncovered the most accurate performance for atmosphere pressure, wind direction, wind speed and rotor speed inputs. However, the forecasting model using Euclidean distance metric for k = 1 brought out the most inconsistent results for atmosphere pressure and wind speed inputs. As a result of multi-tupled analyses, many feasible inferences were achieved for yaw position control systems. In addition, the yaw position forecasting model developed was compared with the persistence model and it surpassed the persistence model significantly in terms of the improvement percent.     

基于数据挖掘技术的天然气价格预测方法研究

天然气价格是影响天然气企业经营决策与运营效益的重要因素,在此背景下,如何准确地预测未来天然气价格自然成为产业界关注的热点话题。此外,在数据挖掘技术快速发展的时代,如何将该技术应用于传统的天然气行业,融入天然气价格的预测当中,也是学术界所探讨的重要话题。基于此,本文首先回顾了以往天然气价格预测方法,然后以传统数据挖掘技术中的模式序列相似性搜索方法(PSS)为基础,通过对该方法中历史序列搜索匹配机制及结果处理机制的改进,提出了一种新的改进模式序列相似性搜索(APSS)天然气价格预测方法。在方法构建之后,采用美国天然气日度现货价格数据对该方法的有效性进行了实验验证。实验结果表明,本文提出的基于数据挖掘技术的APSS方法能够实现对天然气价格的合理预测,且与传统的PSS方法相比,APSS方法的预测结果具有更高的预测精度。     

Forecasting hydrogen production potential in islamabad from solar energy using water electrolysis

The focus of this study is the use of Machine Learning methods to forecast Solar Hydrogen production potential for the Islamabad region of Pakistan. For this purpose, we chose a Photovoltaic-Electrolytic (PV-E) system to forecast electricity and, hence, hydrogen production. The weather data used for forecasting and simulation were recorded with precise meteorological instruments stationed in Islamabad, over the course of 13 and a half months. Out of the three tested algorithms, Prophet performs the best with Mean Absolute Percentage Error of 3.7%, forecasting a daily average Hydrogen production of 93.3 × 10³ kg/Km². Although, the forecast in this study is made for the month of August and September, during which the local season moves towards winter, this study demonstrates solar hydrogen production, as a green energy source, has a tremendous potential in this region.     

基于动态规划法下的矿用自卸车经济寿命预测

采用动态规划法建立了矿用自卸车经济寿命模型,并对其进行了预测,计算结果表明,矿用自卸车的经济寿命约为10年。这与实际情况相一致,这种预测有利于矿用自卸车的运行与维护.     

弱胶结砂层突涌机理及预测研究

第三纪和晚侏罗时期沉积的弱胶结粉细砂层在我国东北，西南地区有广泛的分布。以东北某矿区为例，根据我国矿山存在的井下砂层突涌的现象，利用室内模拟试验，探讨了弱胶结砂层在井下形成突水涌砂的机理，为预测，防止该类事故的发生提供理论依据。     

煤炭需求的灰色预测方法

本文应用灰色预测理论，建立了煤炭需求预测模型，并对我国1996年至2O00年的煤炭需求量进行了预测。经检验，其结果是令人满意的，说明这种方法在煤炭需求预测方面具有一定的实用价值。